For illustrating the background of the invention, particular reference is made to the following publications:    [1] C. Chefd'hotel et al. “Flows of Diffeomorphisms for Multimodal Image Matching”, Proceedings of the IEEE International Symposium on Biomedical Imaging, Washington, USA (2002);    [2] M. Zapke et al. in “Respiratory Research” vol. 7, 2006, p. 106;    [3] M. Deimling et al. in “Proc. Inti. Soc. Mag. Reson. Med.” vol. 16, 2008, p. 2639;    [4] G. Bauman et al. in “Magnetic Resonance in Medicine” vol. 62, 2009, p. 656;    [5] B. B. Avants et al. “Neuroimage” vol. 54, 2011, p. 2033;    [6] A. Kjørstad et al. in “Magn. Reson. Mater. Phy.” 2014, p. 1-10 (DOI 10.1007/s10334-014-0432-9);    [7] C. Schönfeld et al. in “Journal of Magnetic Resonance Imaging”, electronic publication on Sep. 17, 2014;    [8] A. Voskrebenzev et al. “Introduction of global specific ventilation as a reference for specific ventilation derived by Fourier Decomposition in “ISMRM Proceedings”, Milan, 2014;    [9] A. Voskrebenzev et al. “Detection of Chronic Allograft Dysfunction using Ventilation-Weighted Fourier Decomposition Lung MRI” in “ISMRM Proceedings” Toronto, 2015; and    [10] U.S. Pat. No. 8,660,634 B2.
Magnetic resonance tomography (MRT) based lung imaging using Fourier decomposition is described e. g. in [3, 4, 10]. A quantitative ventilation map of the lung can be created by processing MR lung images along the following procedure. A series of two-dimensional (2D) MR lung images is collected with an MRI scanner, e. g. with a temporal resolution of 0.3 s per image for each slice position for a period of 1 min of free ventilation.
Firstly, the MR lung images are subjected to a registration into one image representing a medium ventilation position, using a non-rigid transformation [1, 5] with transformation fields. The registration results in a deformation of the lung images so that all images correspond to the same ventilation position (or: ventilation status). Accordingly, a characteristic temporal signal intensity can be assigned to each lung image voxel.
The registration process as conventionally used in the Fourier decomposition context has the following disadvantages. Images of the expiration ventilation position and the inspiration ventilation position are directly registered into the image representing the medium ventilation position. This requires a strong deformation of lung images resulting in a risk of misregistrations. Furthermore, due to the required temporal resolution, the signal to noise ratio (SNR) of the single images is relatively low. Local lung structures which are important for estimating the transformation fields are superimposed by noise, thus further impeding the registration.
Subsequently, a frequency determination and a separation of ventilation and perfusion contributions in the images is conducted. As the MRI signal is proportional to the proton density, the signal intensity oscillates with the ventilation frequency anti-proportional to the volume change of the lung during the ventilation. Simultaneously, the signal intensity is modulated by blood perfusion with a heart perfusion frequency, which is higher than the ventilation frequency. The ventilation and perfusion frequencies can be determined and separated by a Fourier decomposition of an averaged temporal signal intensity in frequency space [4, 9].
As a limitation of the conventional frequency determination, the averaged temporal signal intensity reliably can be used with healthy subjects only. However, with subjects having a lung disease, abnormal ventilation may occur. This may impede or even exclude an appropriate frequency determination. Furthermore, the Fourier decomposition requires a periodic ventilation and perfusion. With an irregular ventilation, the spectrum of ventilation is broadened in frequency space. Accordingly, the integration limits for calculating the amplitude of ventilation have to be extended, resulting in an increased noise contribution.
Finally, a quantification is obtained by voxel-based integration of image signals at the ventilation and perfusion peaks, resulting in a ventilation or perfusion weighted map of the lung as described in [3] or [4]. Ventilation can be quantified using the parameter fractional ventilation (FV), which is calculated by a ratio of a lung tidal volume (difference of inspiration and expiration volumes) and the inspiration volume.
Conventionally, these volumes are measured e. g. by spirometry. Due to the relationship of signal intensity and volume, it could be considered that this ratio correspondingly can be calculated from the MR lung signal intensities. The integrated ventilation peak would provide the signal change caused by the tidal volume [2]. For calculating the FV, this integration value could be scaled with the expiration signal [6]. However, this approach using MR lung signal intensities does not allow an appropriate quantification process as the fractional ventilation depends on the ventilation deepness, which can vary within the series of MR lung images collected [8]. As the varying ventilation deepness cannot be compensated with the conventional techniques, the calculation of the fractional ventilation based on conventionally processed images cannot yield assessable and reproducible quantitative results.
In summary, the conventional methods mainly suffer from limitations in SNR of registered, filtered and quantified images, so that the conventional quantitative ventilation maps have a limited information contents.